Diagnosis of heart failure from imbalance datasets using multi-level classification

Abstract

Background:The incidence of heart failure is continuing to rise, and the mortality rate is high. Chest X-ray (CXR) has irreplaceable advantages in diagnosing heart failure, such as fast, low risk, and cheap. However, excessive CXR images place a huge burden on physicians and create data imbalance problems. Traditional methods, such as random under-sampling, are used to solve the problems. However, the under-sampling method can destroy the integrity of the data distribution. So, it is necessary to have a method that can address data imbalance problems and assist overburdened healthcare systems. Objective:This study establishes an automatic heart failure diagnosis system based on deep learning from imbalance datasets. Methods:To address the data imbalance problem based on the publicly available CheXpert dataset, this study proposes a method combining under-sampling and instance selection to ensure the integrity of the data distribution. To help physicians better treat heart failure, this study proposes an end-to-end multi-level classification method to diagnose the specific causes of heart failure. Results:On the testing set, our method improves the average accuracy by 3.78% compared to the traditional random under-sampling method, and the accuracy of our end-to-end multi-class classification experiment is 84.44%. Conclusions:The heart failure automated diagnostic system is more efficient and accurate in diagnosing heart failure compared to state-of-the-art methods.